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feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake

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1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试
2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程
3.重整权利声明文件，重整代码工程，确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
This commit is contained in:
wangzhengyang
2022-05-10 09:54:44 +08:00
parent ecdd171c6f
commit 718c41634f
10018 changed files with 3593797 additions and 186748 deletions
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1
3rdparty/opencv-4.5.4/modules/dnn/misc/java/filelist_common vendored Normal file
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misc/java/src/cpp/dnn_converters.hpp

63
3rdparty/opencv-4.5.4/modules/dnn/misc/java/gen_dict.json vendored Normal file
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{
"type_dict": {
"MatShape": {
"j_type": "MatOfInt",
"jn_type": "long",
"jni_type": "jlong",
"jni_var": "MatShape %(n)s",
"suffix": "J",
"v_type": "Mat",
"j_import": "org.opencv.core.MatOfInt"
},
"vector_MatShape": {
"j_type": "List<MatOfInt>",
"jn_type": "List<MatOfInt>",
"jni_type": "jobject",
"jni_var": "std::vector< MatShape > %(n)s",
"suffix": "Ljava_util_List",
"v_type": "vector_MatShape",
"j_import": "org.opencv.core.MatOfInt"
},
"vector_size_t": {
"j_type": "MatOfDouble",
"jn_type": "long",
"jni_type": "jlong",
"jni_var": "std::vector<size_t> %(n)s",
"suffix": "J",
"v_type": "Mat",
"j_import": "org.opencv.core.MatOfDouble"
},
"vector_Ptr_Layer": {
"j_type": "List<Layer>",
"jn_type": "List<Layer>",
"jni_type": "jobject",
"jni_var": "std::vector< Ptr<cv::dnn::Layer> > %(n)s",
"suffix": "Ljava_util_List",
"v_type": "vector_Layer",
"j_import": "org.opencv.dnn.Layer"
},
"vector_Target": {
"j_type": "List<Integer>",
"jn_type": "List<Integer>",
"jni_type": "jobject",
"jni_var": "std::vector< cv::dnn::Target > %(n)s",
"suffix": "Ljava_util_List",
"v_type": "vector_Target"
},
"LayerId": {
"j_type": "DictValue",
"jn_type": "long",
"jn_args": [
[
"__int64",
".getNativeObjAddr()"
]
],
"jni_name": "(*(*(Ptr<cv::dnn::DictValue>*)%(n)s_nativeObj))",
"jni_type": "jlong",
"suffix": "J",
"j_import": "org.opencv.dnn.DictValue"
}
}
}

102
3rdparty/opencv-4.5.4/modules/dnn/misc/java/src/cpp/dnn_converters.cpp vendored Normal file
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
// Author: abratchik
#include "dnn_converters.hpp"
#define LOG_TAG "org.opencv.dnn"
void Mat_to_MatShape(cv::Mat& mat, MatShape& matshape)
{
matshape.clear();
CHECK_MAT(mat.type()==CV_32SC1 && mat.cols==1);
matshape = (MatShape) mat;
}
void MatShape_to_Mat(MatShape& matshape, cv::Mat& mat)
{
mat = cv::Mat(matshape, true);
}
std::vector<MatShape> List_to_vector_MatShape(JNIEnv* env, jobject list)
{
static jclass juArrayList = ARRAYLIST(env);
jmethodID m_size = LIST_SIZE(env, juArrayList);
jmethodID m_get = LIST_GET(env, juArrayList);
static jclass jMatOfInt = MATOFINT(env);
jint len = env->CallIntMethod(list, m_size);
std::vector<MatShape> result;
result.reserve(len);
for (jint i=0; i<len; i++)
{
jobject element = static_cast<jobject>(env->CallObjectMethod(list, m_get, i));
cv::Mat& mat = *((cv::Mat*) GETNATIVEOBJ(env, jMatOfInt, element) );
MatShape matshape = (MatShape) mat;
result.push_back(matshape);
env->DeleteLocalRef(element);
}
return result;
}
jobject vector_Ptr_Layer_to_List(JNIEnv* env, std::vector<cv::Ptr<cv::dnn::Layer> >& vs)
{
static jclass juArrayList = ARRAYLIST(env);
static jmethodID m_create = CONSTRUCTOR(env, juArrayList);
jmethodID m_add = LIST_ADD(env, juArrayList);
static jclass jLayerClass = LAYER(env);
static jmethodID m_create_layer = LAYER_CONSTRUCTOR(env, jLayerClass);
jobject result = env->NewObject(juArrayList, m_create, vs.size());
for (std::vector< cv::Ptr<cv::dnn::Layer> >::iterator it = vs.begin(); it != vs.end(); ++it) {
jobject element = env->NewObject(jLayerClass, m_create_layer, (*it).get());
env->CallBooleanMethod(result, m_add, element);
env->DeleteLocalRef(element);
}
return result;
}
jobject vector_Target_to_List(JNIEnv* env, std::vector<cv::dnn::Target>& vs)
{
static jclass juArrayList = ARRAYLIST(env);
static jmethodID m_create = CONSTRUCTOR(env, juArrayList);
jmethodID m_add = LIST_ADD(env, juArrayList);
static jclass jInteger = env->FindClass("java/lang/Integer");
static jmethodID m_create_Integer = env->GetMethodID(jInteger, "<init>", "(I)V");
jobject result = env->NewObject(juArrayList, m_create, vs.size());
for (size_t i = 0; i < vs.size(); ++i)
{
jobject element = env->NewObject(jInteger, m_create_Integer, vs[i]);
env->CallBooleanMethod(result, m_add, element);
env->DeleteLocalRef(element);
}
return result;
}
std::vector<cv::Ptr<cv::dnn::Layer> > List_to_vector_Ptr_Layer(JNIEnv* env, jobject list)
{
static jclass juArrayList = ARRAYLIST(env);
jmethodID m_size = LIST_SIZE(env, juArrayList);
jmethodID m_get = LIST_GET(env, juArrayList);
static jclass jLayerClass = LAYER(env);
jint len = env->CallIntMethod(list, m_size);
std::vector< cv::Ptr<cv::dnn::Layer> > result;
result.reserve(len);
for (jint i=0; i<len; i++)
{
jobject element = static_cast<jobject>(env->CallObjectMethod(list, m_get, i));
cv::Ptr<cv::dnn::Layer>* layer_ptr = (cv::Ptr<cv::dnn::Layer>*) GETNATIVEOBJ(env, jLayerClass, element) ;
cv::Ptr<cv::dnn::Layer> layer = *(layer_ptr);
result.push_back(layer);
env->DeleteLocalRef(element);
}
return result;
}

33
3rdparty/opencv-4.5.4/modules/dnn/misc/java/src/cpp/dnn_converters.hpp vendored Normal file
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
// Author: abratchik
#ifndef DNN_CONVERTERS_HPP
#define DNN_CONVERTERS_HPP
#include <jni.h>
#include "opencv_java.hpp"
#include "opencv2/core.hpp"
#include "opencv2/dnn/dnn.hpp"
#define LAYER(ENV) static_cast<jclass>(ENV->NewGlobalRef(ENV->FindClass("org/opencv/dnn/Layer")))
#define LAYER_CONSTRUCTOR(ENV, CLS) ENV->GetMethodID(CLS, "<init>", "(J)V")
using namespace cv::dnn;
void Mat_to_MatShape(cv::Mat& mat, MatShape& matshape);
void MatShape_to_Mat(MatShape& matshape, cv::Mat& mat);
std::vector<MatShape> List_to_vector_MatShape(JNIEnv* env, jobject list);
jobject vector_Ptr_Layer_to_List(JNIEnv* env, std::vector<cv::Ptr<cv::dnn::Layer> >& vs);
std::vector<cv::Ptr<cv::dnn::Layer> > List_to_vector_Ptr_Layer(JNIEnv* env, jobject list);
jobject vector_Target_to_List(JNIEnv* env, std::vector<cv::dnn::Target>& vs);
#endif /* DNN_CONVERTERS_HPP */

119
3rdparty/opencv-4.5.4/modules/dnn/misc/java/test/DnnListRegressionTest.java vendored Normal file
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package org.opencv.test.dnn;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfByte;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.DictValue;
import org.opencv.dnn.Dnn;
import org.opencv.dnn.Layer;
import org.opencv.dnn.Net;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.test.OpenCVTestCase;
/*
* regression test for #12324,
* testing various java.util.List invocations,
* which use the LIST_GET macro
*/
public class DnnListRegressionTest extends OpenCVTestCase {
private final static String ENV_OPENCV_DNN_TEST_DATA_PATH = "OPENCV_DNN_TEST_DATA_PATH";
private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
String modelFileName = "";
String sourceImageFile = "";
Net net;
@Override
protected void setUp() throws Exception {
super.setUp();
String envDnnTestDataPath = System.getenv(ENV_OPENCV_DNN_TEST_DATA_PATH);
if(envDnnTestDataPath == null){
isTestCaseEnabled = false;
return;
}
File dnnTestDataPath = new File(envDnnTestDataPath);
modelFileName = new File(dnnTestDataPath, "dnn/tensorflow_inception_graph.pb").toString();
String envTestDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
if(envTestDataPath == null) throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");
File testDataPath = new File(envTestDataPath);
File f = new File(testDataPath, "dnn/grace_hopper_227.png");
sourceImageFile = f.toString();
if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);
net = Dnn.readNetFromTensorflow(modelFileName);
Mat image = Imgcodecs.imread(sourceImageFile);
assertNotNull("Loading image from file failed!", image);
Mat inputBlob = Dnn.blobFromImage(image, 1.0, new Size(224, 224), new Scalar(0), true, true);
assertNotNull("Converting image to blob failed!", inputBlob);
net.setInput(inputBlob, "input");
}
public void testSetInputsNames() {
List<String> inputs = new ArrayList();
inputs.add("input");
try {
net.setInputsNames(inputs);
} catch(Exception e) {
fail("Net setInputsNames failed: " + e.getMessage());
}
}
public void testForward() {
List<Mat> outs = new ArrayList();
List<String> outNames = new ArrayList();
outNames.add("softmax2");
try {
net.forward(outs,outNames);
} catch(Exception e) {
fail("Net forward failed: " + e.getMessage());
}
}
public void testGetMemoryConsumption() {
int layerId = 1;
List<MatOfInt> netInputShapes = new ArrayList();
netInputShapes.add(new MatOfInt(1, 3, 224, 224));
long[] weights=null;
long[] blobs=null;
try {
net.getMemoryConsumption(layerId, netInputShapes, weights, blobs);
} catch(Exception e) {
fail("Net getMemoryConsumption failed: " + e.getMessage());
}
}
public void testGetFLOPS() {
int layerId = 1;
List<MatOfInt> netInputShapes = new ArrayList();
netInputShapes.add(new MatOfInt(1, 3, 224, 224));
try {
net.getFLOPS(layerId, netInputShapes);
} catch(Exception e) {
fail("Net getFLOPS failed: " + e.getMessage());
}
}
}

149
3rdparty/opencv-4.5.4/modules/dnn/misc/java/test/DnnTensorFlowTest.java vendored Normal file
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package org.opencv.test.dnn;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfByte;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.DictValue;
import org.opencv.dnn.Dnn;
import org.opencv.dnn.Layer;
import org.opencv.dnn.Net;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.test.OpenCVTestCase;
public class DnnTensorFlowTest extends OpenCVTestCase {
private final static String ENV_OPENCV_DNN_TEST_DATA_PATH = "OPENCV_DNN_TEST_DATA_PATH";
private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
String modelFileName = "";
String sourceImageFile = "";
Net net;
private static void normAssert(Mat ref, Mat test) {
final double l1 = 1e-5;
final double lInf = 1e-4;
double normL1 = Core.norm(ref, test, Core.NORM_L1) / ref.total();
double normLInf = Core.norm(ref, test, Core.NORM_INF) / ref.total();
assertTrue(normL1 < l1);
assertTrue(normLInf < lInf);
}
@Override
protected void setUp() throws Exception {
super.setUp();
String envDnnTestDataPath = System.getenv(ENV_OPENCV_DNN_TEST_DATA_PATH);
if(envDnnTestDataPath == null){
isTestCaseEnabled = false;
return;
}
File dnnTestDataPath = new File(envDnnTestDataPath);
modelFileName = new File(dnnTestDataPath, "dnn/tensorflow_inception_graph.pb").toString();
String envTestDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
if(envTestDataPath == null) throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");
File testDataPath = new File(envTestDataPath);
File f = new File(testDataPath, "dnn/grace_hopper_227.png");
sourceImageFile = f.toString();
if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);
net = Dnn.readNetFromTensorflow(modelFileName);
}
public void testGetLayerTypes() {
List<String> layertypes = new ArrayList();
net.getLayerTypes(layertypes);
assertFalse("No layer types returned!", layertypes.isEmpty());
}
public void testGetLayer() {
List<String> layernames = net.getLayerNames();
assertFalse("Test net returned no layers!", layernames.isEmpty());
String testLayerName = layernames.get(0);
DictValue layerId = new DictValue(testLayerName);
assertEquals("DictValue did not return the string, which was used in constructor!", testLayerName, layerId.getStringValue());
Layer layer = net.getLayer(layerId);
assertEquals("Layer name does not match the expected value!", testLayerName, layer.get_name());
}
public void checkInceptionNet(Net net)
{
Mat image = Imgcodecs.imread(sourceImageFile);
assertNotNull("Loading image from file failed!", image);
Mat inputBlob = Dnn.blobFromImage(image, 1.0, new Size(224, 224), new Scalar(0), true, true);
assertNotNull("Converting image to blob failed!", inputBlob);
net.setInput(inputBlob, "input");
Mat result = new Mat();
try {
net.setPreferableBackend(Dnn.DNN_BACKEND_OPENCV);
result = net.forward("softmax2");
}
catch (Exception e) {
fail("DNN forward failed: " + e.getMessage());
}
assertNotNull("Net returned no result!", result);
result = result.reshape(1, 1);
Core.MinMaxLocResult minmax = Core.minMaxLoc(result);
assertEquals("Wrong prediction", (int)minmax.maxLoc.x, 866);
Mat top5RefScores = new MatOfFloat(new float[] {
0.63032645f, 0.2561979f, 0.032181446f, 0.015721032f, 0.014785315f
}).reshape(1, 1);
Core.sort(result, result, Core.SORT_DESCENDING);
normAssert(result.colRange(0, 5), top5RefScores);
}
public void testTestNetForward() {
checkInceptionNet(net);
}
public void testReadFromBuffer() {
File modelFile = new File(modelFileName);
byte[] modelBuffer = new byte[ (int)modelFile.length() ];
try {
FileInputStream fis = new FileInputStream(modelFile);
fis.read(modelBuffer);
fis.close();
} catch (IOException e) {
fail("Failed to read a model: " + e.getMessage());
}
net = Dnn.readNetFromTensorflow(new MatOfByte(modelBuffer));
checkInceptionNet(net);
}
public void testGetAvailableTargets() {
List<Integer> targets = Dnn.getAvailableTargets(Dnn.DNN_BACKEND_OPENCV);
assertTrue(targets.contains(Dnn.DNN_TARGET_CPU));
}
}